Method to dynamically adjusting steering rates of autonomous vehicles

ABSTRACT

In one embodiment, a number of steering rate candidates are determined for a steering control command of operating an autonomous vehicle. For each of the steering rate candidates, a steering rate cost is calculated for the steering rate candidate by applying a predetermined cost function, including calculating a first cost for the steering rate candidate based on a difference between a target steering position and a current steering position of the autonomous vehicle using a first predetermined cost function. One of the steering rate candidates having a lowest steering rate cost is selected as a target steering rate. A steering control command is generated based on the selected steering rate candidate to control a steering wheel of the autonomous vehicle.

RELATED APPLICATION

The application is a continuation of U.S. patent application Ser. No.15/358,078, filed Nov. 21, 2016, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

Embodiments of the present invention relate generally to operatingautonomous vehicles. More particularly, embodiments of the inventionrelate to dynamically adjusting steering rates of autonomous vehicles.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Motion planning and control are critical operations in autonomousdriving. However, conventional motion planning operations estimate thedifficulty of completing a given path mainly from its curvature andspeed, without considering the differences in features for differenttypes of vehicles. Same motion planning and control is applied to alltypes of vehicles, which may not be accurate and smooth under somecircumstances.

In addition, steer control is a critical step in autonomous driving.When seeking a steer control accuracy, one usually requires a highsteering rate (also referred to as a steering moving speed). However, ahigh steering rate may be dangerous to both the vehicle steering systemand passengers, as well as overshoot during the operation.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment of the invention.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment of the invention.

FIG. 3 is a block diagram illustrating an example of a perception andplanning system used with an autonomous vehicle according to oneembodiment of the invention.

FIG. 4 is a block diagram illustrating a control module according to oneembodiment of the invention.

FIG. 5 is a data structure illustrating a process of determiningsteering rates according to one embodiment of the invention.

FIG. 6 is a flow diagram illustrating a process of a determiningsteering rate for operating an autonomous vehicle according to oneembodiment of the invention.

FIG. 7 is a flow diagram illustrating a process of a determiningsteering rate for operating an autonomous vehicle according to anotherembodiment of the invention.

FIG. 8 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the inventions will be described withreference to details discussed below, and the accompanying drawings willillustrate the various embodiments. The following description anddrawings are illustrative of the invention and are not to be construedas limiting the invention. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentinvention. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present inventions.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a steering rate determination system orpredictive model is provided to determine or recommend a steering ratebased on the vehicle control information as planned at the point intime. The steering rate determination system or predictive model may becreated based on a large set of driving statistics captured from avariety of vehicles driven under a variety of driving conditions, whichmay be trained using a machine learning system. The vehicle controlinformation may include a current target steering position, a currentsteering position, a previous target steering position, a previoussteering position, and/or a current vehicle speed at the point in time.The vehicle control information may be captured additionally by avariety of sensors of the vehicle at real-time while the vehicle is inmotion. The output of the system or predictive model represents arecommended steering rate (also referred to as a target steering rate),which may be utilized to generate a steering control command for asubsequent command cycle or cycles. A steering rate refers to a turningspeed of a steering wheel of a vehicle, for example, in a form ofdegrees per second.

In one embodiment, a set of steering rate candidates is determined,which represent the possible steering rates for a particular autonomousvehicle or particular type of autonomous vehicles. The set of steeringrate candidates may be predetermined and they may vary from vehicles tovehicles. For each of the steering rate candidates, one or moreindividual costs are calculated for the steering rate candidate usingone or more cost functions. Each cost function corresponds to one of thecost categories to be taken into consideration when determining a targetsteering rate for one or more subsequent command cycles. A total cost iscalculated based on the individual costs for each steering ratecandidate. After all of the total costs of all steering rate candidateshave been determined, one of the steering rate candidates having thelowest total cost is selected as the target steering rate for thesubsequent command cycle or cycles.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the invention. Referring toFIG. 1, network configuration 100 includes autonomous vehicle 101 thatmay be communicatively coupled to one or more servers 103-104 over anetwork 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113,infotainment system 114, and sensor system 115. Autonomous vehicle 101may further include certain common components included in ordinaryvehicles, such as, an engine, wheels, steering wheel, transmission,etc., which may be controlled by vehicle control system 111 and/orperception and planning system 110 using a variety of communicationsignals and/or commands, such as, for example, acceleration signals orcommands, deceleration signals or commands, steering signals orcommands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 performs ortrains a set of algorithms or predictive models 124 for a variety ofpurposes. In one embodiment, machine learning engine 122 generates a setof one or more cost functions or cost predictive models 124 to determineor predict a cost for a particular steering rate to be targeted. Costfunctions 124 may include one or more individual cost functions tocalculate one or more individual costs of one or more cost category.Cost functions 124 are designed to calculate individual costs fortargeting a specific steering rate. In one embodiment, cost functions124 include a first cost function to determine a first cost based on atarget steering position and a current steering position. Cost functions124 may further include a second cost function to determine a secondcost based on a past target steering position and a past steeringposition in a previous command cycle. Cost functions 124 may furtherinclude a third cost function to determine a third cost based on thecurrent vehicle speed. Cost functions 124 may further include a fourthcost function to determine a fourth cost based on a target steeringrate.

In one embodiment, cost functions 124 may be determined and created bymachine learning engine 122 based on driving statistics 123, which werecollected from a variety of vehicles. Machine learning engine 122 mayexamine the driving statistics of autonomous driving and compare thosewith the driving statistics of the vehicles driven by human driversunder the same or similar driving circumstances. The difference betweenthe autonomous driving and the human driving may determine the cost forthe driving parameters, in this example, steering rates, in order toachieve the same or similar results as of human drivers. In oneembodiment, if the difference between autonomous driving and humandriving is small, the cost may be lower, or vice versa. Once the costfunctions 124 have been created, they can be uploaded to autonomousvehicles to be used to dynamically adjust the steering rates as plannedat real-time, such that the turning of the vehicles can be morecomfortable and smooth.

FIG. 3 is a block diagram illustrating an example of a perception andplanning system used with an autonomous vehicle according to oneembodiment of the invention. System 300 may be implemented as a part ofautonomous vehicle 101 of FIG. 1 including, but is not limited to,perception and planning system 110, control system 111, and sensorsystem 115. Referring to FIG. 3, perception and planning system 110includes, but is not limited to, localization module 301, perceptionmodule 302, decision module 303, planning module 304, and control module305.

Some or all of modules 301-307 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-307may be integrated together as an integrated module.

Localization module 301 (also referred to as a map and route module)manages any data related to a trip or route of a user. A user may log inand specify a starting location and a destination of a trip, forexample, via a user interface. Localization module 301 communicates withother components of autonomous vehicle 300, such as map and routeinformation 311, to obtain the trip related data. For example,localization module 301 may obtain location and route information from alocation server and a map and POI (MPOI) server. A location serverprovides location services and an MPOI server provides map services andthe POIs of certain locations, which may be cached as part of map androute information 311. While autonomous vehicle 300 is moving along theroute, localization module 301 may also obtain real-time trafficinformation from a traffic information system or server.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, decision module 303 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module303 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 303 may make such decisions according to a set ofrules such as driving or traffic rules 312, which may be stored inpersistent storage device 352.

Based on a decision for each of the objects perceived, planning module304 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle). That is, for agiven object, decision module 303 decides what to do with the object,while planning module 304 determines how to do it. For example, for agiven object, decision module 303 may decide to pass the object, whileplanning module 304 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 304 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 305 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

Note that decision module 303 and planning module 304 may be integratedas an integrated module. Decision module 303/planning module 304 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to effect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Decision module 303/planning module 304 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

According to one embodiment, control module 305 includes steeringcontrol module 306 to control a steering wheel of the vehicle. Inresponse to a request to change a direction of the vehicle, which may beplanned by planning module 304, steering control module 306 determines aset of one or more steering rate candidates as the potential targetsteering rates. The steering rate candidates may be previouslydetermined for the vehicle or the type of vehicles and stored inpersistent storage device 352. For each of the steering rate candidates,steering control module 306 determines a cost of issuing a steeringcontrol command based on the steering rate candidate as a potentialtarget steering rate using one or more of cost functions 124. Steeringcontrol module 306 then selects one of the steering rate candidateshaving the lowest total cost as the target steering rate for asubsequent command cycle.

A cost associated with a steering rate candidate represents a difficultylevel at which the vehicle is able to turn as planned under thecircumstances. A high cost indicates that the vehicle may have a higherdifficulty level to achieve the goal of turning based on the steeringcandidate in question. Alternatively, the cost represents a comfortlevel at which a passenger is when the vehicle turns according to thesteering rate candidate in question under the circumstances. A lowercost indicates that the passenger may feel more comfortable when thevehicle turns according to the steering rate candidate in question.

FIG. 4 is a block diagram illustrating a control module according to oneembodiment of the invention. Referring to FIG. 4, control module 305includes a steering control module 306, current position-based costmodule 401, past position-based cost module 402, vehicle speed-basedcost module 403, and target rate-based cost module 404. Each of costmodules 401-404 calculates an individual cost based on a particular costcategory using a corresponding one of the cost functions, such as,current position-based cost function 411, past position-based costfunction 412, vehicle speed cost function 413, and target rate costfunction 414. Cost functions 411-414 may be created based on priordriving statistics by a machine learning engine such as machine learningengine 122 of FIG. 1. Although there are only four cost functionsrepresenting four different cost categories, more or fewer costfunctions can also be utilized.

In one embodiment, in response to a request to determine a steeringrate, a set of one or more steering rate candidates is determined orselected. For each of the steering rate candidates, steering controlmodule 306 invokes one or more of cost modules 401-404 to calculate oneor more individual costs for their respective cost categories using oneor more cost functions 411-414. A total cost for the steering rate isthen calculated based on the individual costs generated by the costmodules 401-404 using at least some of cost functions 411-414. One ofthe steering rate candidates having the lowest total cost is selected asa target steering rate for a subsequent command cycle.

In one embodiment, current position-based cost function 411 is used tocalculate a cost based on a current steering position and a targetsteering position in view of the steering rate candidate in questionrepresenting a potential target steering rate. In one embodiment, costfunction 411 is used to calculate a cost based on a difference betweenthe current steering position and the target steering position in viewof the target steering rate in question. In one embodiment, if thetarget steering rate is greater than or equals to the difference betweenthe current steering position and the target steering position, the costis assigned as zero. Otherwise, if the target steering rate is less thanthe difference between the current steering position and the targetsteering position, the cost is assigned with the difference between thecurrent steering position and the target steering position minus thetarget steering rate (e.g. steering rate candidate in question) asfollows:Cost=Diff(target position, current position)−Target Steering Rate

Referring now to FIG. 5, which is a data structure illustrating costscalculated using different cost functions for a set of steering ratecandidates 501, costs 502 are then calculated for the steering ratecandidates 501. In this example, it is assumed the difference betweenthe current steering position and the target steering position in acurrent command cycle is 300. Thus, for steering rate candidate of 100as a potential target steering rate, based on the current position-basedcost function 411 as described above, since the difference of 300 isgreater than the potential target steering rate of 100, therefore thecost will be 200 (i.e., the difference of 300 subtracts the targetsteering rate of 100). Similarly, the cost for steering rate candidateof 200 will be 100, while the cost for candidates of 300, 400, and 500will be zero since they are greater than or equal to the difference of300.

According to one embodiment, past position-based cost function 412 is tocalculate a cost based on the previous target steering position and theprevious steering position in a previous command cycle, i.e., thedifference between the “current” steering position and the targetsteering position of the previous command cycle. In one embodiment, aprevious command cycle refers to the last command cycle immediatelybefore the current command cycle. In a further embodiment, the cost iscalculated based on a relationship between the current steering ratecandidate and the maximum steering rate candidate. In one embodiment,for a given steering rate candidate, the past position-based cost 503 iscalculated as follows:Cost=Difference*(Max Candidate−Current Candidate)/Max Candidate

Difference herein refers to the difference between a target steeringposition and a “current” steering position in a previous command cycle.The “current” candidate refers to the candidate of which the cost is tobe calculated.

Referring to FIG. 5, in this example, it is assumed the differencebetween the past steering target position and the past steering positionis 100. For steering rate of 100, the cost will be 100*(500−100)/500=80,where the maximum steering rate candidate is 500 in this example. Thecost for the remaining candidates of 200, 300, 400, and 500 can also becalculated using the above algorithm as of 60, 40, 20, and 0.

According to one embodiment, the vehicle speed-based cost 504 iscalculated based on the current vehicle speed in view of the currentsteering rate candidate. In a particular embodiment, the cost can becalculated as follows:Cost=current vehicle speed*current steering rate candidate/100Referring back to FIG. 5, it is assumed that the current vehicle speedis 30 mile per hour (mph). Thus, the cost for candidate of 100 will be30*100/100=30. Similarly, the costs for candidates of 200, 300, 400, and500 are 60, 90, 120, and 150 respectively.

According to one embodiment, the target rate-based cost 505 can becalculated using a linear cost function. The rationale behind it is thatthe cost is proportional to the target steering rate or steering speed:as higher steering rate costs more. For simplicity, in one embodiment,the cost for a given steering rate candidate, the target rate-based cost505 equals to the steering rate candidate itself as shown in FIG. 5.

After all individual costs 502-505 have been calculated for each of thesteering rate candidates 501, total cost 506 for each of the steeringrate candidates 501 is calculated, for example, by summing individualcosts 502-505. One of the steering rate candidates 501 having the lowesttotal cost (in this example, candidate of 100) is then selected as thetarget steering rate for a subsequent command cycle for steeringcontrol.

FIG. 6 is a flow diagram illustrating a process of a determiningsteering rate for operating an autonomous vehicle according to oneembodiment of the invention. Process 600 may be performed by processinglogic which may include software, hardware, or a combination thereof.For example, process 600 may be performed by control module 305.Referring to FIG. 6, in operation 601, processing logic determines anumber of steering rate candidates for steering control of an ADV. Foreach of the steering rate candidates, in operation 602, processing logicapplies one or more cost functions to calculate one or more individualcosts representing an impact of the steering rate candidate to operatingthe ADV. Each cost functions corresponds to one of the cost categories.In operation 603, a total cost is determined based on the individualcosts for each of the steering rate candidates. In operation 604, one ofthe steering rate candidates having the lowest total cost is selected asa target steering rate for a subsequent command cycle for steeringcontrol of the ADV. In operation 605, a steering control command isgenerated based on the target steering rate to control a steering wheelof the ADV.

FIG. 7 is a flow diagram illustrating a process of a determiningsteering rate for operating an autonomous vehicle according to anotherembodiment of the invention. Process 700 may be performed by processinglogic which may include software, hardware, or a combination thereof.For example, process 700 may be performed by control module 305 as partof operations 602-603 of FIG. 6. Referring to FIG. 7, in operation 701,processing logic calculates a first cost based on a target steeringposition and a current steering position (of the current command cycle)using a first cost function. If the difference between the target andcurrent steering positions is less than the target steering rate (e.g.,current steering rate candidate in question), the first cost is zero;otherwise, the first cost equals to the difference between the targetand current steering positions minus the target steering rate. Inoperation 702, processing logic calculates a second cost based on a pasttarget steering position and a past steering position of a previouscommand cycle using a second cost function. In operation 703, processinglogic calculates a third cost based on a current vehicle speed of theADV using a third cost function. In operation 704, processing logiccalculates a fourth cost based on the target steering rate (e.g.,steering rate candidate in question) using a fourth cost function. Inoperation 705, a total cost is calculated based on the first cost, thesecond cost, the third cost, and the fourth cost.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 8 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the invention. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, data processing system 110 or any ofservers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 via a bus or an interconnect 1510. Processor 1501 mayrepresent a single processor or multiple processors with a singleprocessor core or multiple processor cores included therein. Processor1501 may represent one or more general-purpose processors such as amicroprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, control module 305. Processingmodule/unit/logic 1528 may also reside, completely or at leastpartially, within memory 1503 and/or within processor 1501 duringexecution thereof by data processing system 1500, memory 1503 andprocessor 1501 also constituting machine-accessible storage media.Processing module/unit/logic 1528 may further be transmitted or receivedover a network via network interface device 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present invention. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present invention. It willalso be appreciated that network computers, handheld computers, mobilephones, servers, and/or other data processing systems which have fewercomponents or perhaps more components may also be used with embodimentsof the invention.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the invention also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present invention are not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the invention as described herein.

In the foregoing specification, embodiments of the invention have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the invention as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for determining asteering rate for operating an autonomous vehicle, the methodcomprising: determining a plurality of steering rate candidates for asteering control command of operating an autonomous vehicle; for each ofthe steering rate candidates, calculating a steering rate cost for thesteering rate candidate by applying a predetermined cost function,including calculating a first cost for the steering rate candidate basedon a difference between a target steering position and a currentsteering position of the autonomous vehicle using a first predeterminedcost function; selecting one of the steering rate candidates having alowest steering rate cost as a target steering rate; and generating asteering control command based on the selected steering rate candidateto control a steering wheel of the autonomous vehicle.
 2. The method ofclaim 1, wherein calculating a first cost for the steering ratecandidate comprises: assigning the first cost as the difference betweenthe target steering position and the current steering position minus thesteering rate candidate if the steering rate candidate is less than thedifference; and assigning the first individual cost as a predeterminedvalue other than the difference between the target steering position andthe current steering position, if the steering rate candidate is greaterthan or equal to the difference.
 3. The method of claim 1, whereincalculating a steering rate cost for the steering rate candidate furthercomprises calculating a second cost based on a difference between aprevious target steering position and a previous steering position ofthe autonomous vehicle for a previous command cycle using a second costfunction, and wherein the steering rate cost for the steering ratecandidate is determined based on the first cost and the second cost. 4.The method of claim 3, wherein the second cost is calculated based on adifference between the steering rate candidate and a highest steeringrate candidate in view of the difference between the previous targetsteering position and the previous steering position.
 5. The method ofclaim 1, wherein calculating a steering rate cost for the steering ratecandidate further comprises calculating a third cost based on a currentvehicle speed of the autonomous vehicle in view of the steering ratecandidate as a target steering rate using a third cost function, andwherein the steering rate cost for the steering rate candidate isdetermined based on the first cost and the third cost.
 6. The method ofclaim 5, wherein the third cost is calculated by multiplying the vehiclespeed and the steering rate candidate divided by a constant.
 7. Themethod of claim 1, wherein calculating a steering rate cost for thesteering rate candidate further comprises calculating a fourth costbased on the steering rate candidate as a target steering rate using afourth cost function, wherein the steering rate cost for the steeringrate candidate is determined based on the first cost and the fourthcost.
 8. A non-transitory machine-readable medium having instructionsstored therein, which when executed by a processor, cause the processorto perform operations, the operations comprising: determining aplurality of steering rate candidates for a steering control command ofoperating an autonomous vehicle; for each of the steering ratecandidates, calculating a steering rate cost for the steering ratecandidate by applying a predetermined cost function, includingcalculating a first cost for the steering rate candidate based on adifference between a target steering position and a current steeringposition of the autonomous vehicle using a first predetermined costfunction; selecting one of the steering rate candidates having a loweststeering rate cost as a target steering rate; and generating a steeringcontrol command based on the selected steering rate candidate to controla steering wheel of the autonomous vehicle.
 9. The machine-readablemedium of claim 8, wherein calculating a first cost for the steeringrate candidate comprises: assigning the first cost as the differencebetween the target steering position and the current steering positionminus the steering rate candidate if the steering rate candidate is lessthan the difference; and assigning the first individual cost as apredetermined value other than the difference between the targetsteering position and the current steering position, if the steeringrate candidate is greater than or equal to the difference.
 10. Themachine-readable medium of claim 8, wherein calculating a steering ratecost for the steering rate candidate further comprises calculating asecond cost based on a difference between a previous target steeringposition and a previous steering position of the autonomous vehicle fora previous command cycle using a second cost function, and wherein thesteering rate cost for the steering rate candidate is determined basedon the first cost and the second cost.
 11. The machine-readable mediumof claim 10, wherein the second cost is calculated based on a differencebetween the steering rate candidate and a highest steering ratecandidate in view of the difference between the previous target steeringposition and the previous steering position.
 12. The machine-readablemedium of claim 8, wherein calculating a steering rate cost for thesteering rate candidate further comprises calculating a third cost basedon a current vehicle speed of the autonomous vehicle in view of thesteering rate candidate as a target steering rate using a third costfunction, and wherein the steering rate cost for the steering ratecandidate is determined based on the first cost and the third cost. 13.The machine-readable medium of claim 12, wherein the third cost iscalculated by multiplying the vehicle speed and the steering ratecandidate divided by a constant.
 14. The machine-readable medium ofclaim 8, wherein calculating a steering rate cost for the steering ratecandidate further comprises calculating a fourth cost based on thesteering rate candidate as a target steering rate using a fourth costfunction, wherein the steering rate cost for the steering rate candidateis determined based on the first cost and the fourth cost.
 15. A dataprocessing system, comprising: a processor; and a memory coupled to theprocessor to store instructions, which when executed by the processor,cause the processor to perform operations, the operations includingdetermining a plurality of steering rate candidates for a steeringcontrol command of operating an autonomous vehicle, for each of thesteering rate candidates, calculating a steering rate cost for thesteering rate candidate by applying a predetermined cost function,including calculating a first cost for the steering rate candidate basedon a difference between a target steering position and a currentsteering position of the autonomous vehicle using a first predeterminedcost function, selecting one of the steering rate candidates having alowest steering rate cost as a target steering rate, and generating asteering control command based on the selected steering rate candidateto control a steering wheel of the autonomous vehicle.
 16. The system ofclaim 15, wherein calculating a first cost for the steering ratecandidate comprises: assigning the first cost as the difference betweenthe target steering position and the current steering position minus thesteering rate candidate if the steering rate candidate is less than thedifference; and assigning the first individual cost as a predeterminedvalue other than the difference between the target steering position andthe current steering position, if the steering rate candidate is greaterthan or equal to the difference.
 17. The system of claim 15, whereincalculating a steering rate cost for the steering rate candidate furthercomprises calculating a second cost based on a difference between aprevious target steering position and a previous steering position ofthe autonomous vehicle for a previous command cycle using a second costfunction, and wherein the steering rate cost for the steering ratecandidate is determined based on the first cost and the second cost. 18.The system of claim 17, wherein the second cost is calculated based on adifference between the steering rate candidate and a highest steeringrate candidate in view of the difference between the previous targetsteering position and the previous steering position.
 19. The system ofclaim 15, wherein calculating a steering rate cost for the steering ratecandidate further comprises calculating a third cost based on a currentvehicle speed of the autonomous vehicle in view of the steering ratecandidate as a target steering rate using a third cost function, andwherein the steering rate cost for the steering rate candidate isdetermined based on the first cost and the third cost.
 20. The system ofclaim 19, wherein the third cost is calculated by multiplying thevehicle speed and the steering rate candidate divided by a constant. 21.The system of claim 15, wherein calculating a steering rate cost for thesteering rate candidate further comprises calculating a fourth costbased on the steering rate candidate as a target steering rate using afourth cost function, wherein the steering rate cost for the steeringrate candidate is determined based on the first cost and the fourthcost.